Database table format conversion based on user data access patterns in a networked computing environment

ABSTRACT

An approach for conversion between database formats (e.g., from a relational database format to a hash table or a “big table” database format) based on user data access patterns in a networked computing environment is provided. A first set of database tables having a first format is identified based on a set of access patterns stored in a computer storage device. A second set of database tables having a second database format corresponding to the first set of database tables may then be provided (e.g., accessed, augmented, and/or generated). A mapping between the first set of database tables and the second set of database tables may then be created. A column set may then be generated based on at least one condition of the set of queries. The column set may then be used as a key for the second set of database tables.

RELATED APPLICATION

This patent document is a continuation of, and claims the benefit of,co-pending and co-owned U.S. patent application Ser. No. 14/689,377,filed Apr. 17, 2015, which is a continuation of commonly owned U.S.patent application Ser. No. 13/599,498, filed Aug. 30, 2012, issued Jun.9, 2015 as U.S. Pat. No. 9,053,161, the entire contents of which areherein incorporated by reference.

TECHNICAL FIELD

In general, the present invention relates to database table formatconversion. Specifically, embodiments of the present invention relate togeneration of a hash table (a.k.a., “big table”) database table from oneor more relational database tables based on user access patterns (e.g.,queries, insertions, updates or deletions) in a networked computingenvironment (e.g., a cloud computing environment).

BACKGROUND

The networked computing environment (e.g., cloud computing environment)is an enhancement to the predecessor grid environment, whereby multiplegrids and other computation resources may be further enhanced by one ormore additional abstraction layers (e.g., a cloud layer), thus makingdisparate devices appear to an end-consumer as a single pool of seamlessresources. These resources may include such things as physical orlogical computing engines, servers and devices, device memory, andstorage devices, among others.

Cloud computing models provide a convenient tool for applicationhosting. Challenges may exist, however, in applying cloud computingmodels to database architectures because many databases hosted in cloudenvironments follow a tabular database model. That is, a traditionalrelational database model may not be applicable in designing anapplication that is to be hosted in a cloud environment. Accordingly, itoften falls upon a user/customer to comprehend the concepts involvedwith a tabular database format. As such, conversion from a relationaldatabase format to a tabular database format is often performedmanually, which can be time consuming and prone to error.

SUMMARY

In general, embodiments of the present invention relate to approachesfor conversion between database formats (e.g., from a relationaldatabase format to a hash table or a “big table” database format) basedon user data access patterns (e.g., a set of queries, insertions,updates, or deletions) in a networked computing environment (e.g., acloud computing environment). In a typical embodiment, a first set ofdatabase tables having a first format is identified based on a set ofaccess patterns stored in a computer storage device. A second set ofdatabase tables having a second database format corresponding to thefirst set of database tables may then be provided (e.g., accessed,augmented and/or generated). A mapping between the first set of databasetables and the second set of database tables may then be created. Acolumn set may then be generated based on at least one condition of theset of access patterns. The column set may then be used as a key for thesecond set of database tables.

A first aspect of the present invention provides a computer-implementedmethod for converting database formats based on user data accesspatterns in a networked computing environment, comprising: identifying afirst set of database tables based on a set of access patterns stored ina computer storage device, the first set of database tables having afirst format, and the set of access patterns indicating a set of userdata queries, insertions, updates or deletions to data stored in thefirst set of database tables; providing a second set of database tableshaving a second database format corresponding to the first set ofdatabase tables; creating a mapping between the first set of databasetables and the second set of database tables; and generating a key forthe second set of database tables based on at least one condition of theset of access patterns.

A second aspect of the present invention provides a system forconverting database formats based on user data access patterns in anetworked computing environment, comprising: a memory medium comprisinginstructions; a bus coupled to the memory medium; and a processorcoupled to the bus that when executing the instructions causes thesystem to: identify a first set of database tables based on a set ofqueries stored in a computer storage device, the first set of databasetables having a first format, and the set of access patterns indicatinga set of user data queries, insertions, updates or deletions to datastored in the first set of database tables; provide a second set ofdatabase tables having a second database format corresponding to thefirst set of database tables; create a mapping between the first set ofdatabase tables and the second set of database tables; and generate akey for the second set of database tables based on at least onecondition of the set of access patterns.

A third aspect of the present invention provides a computer programproduct for converting database formats based on user data accesspatterns in a networked computing environment; the computer programproduct comprising a computer readable storage media, and programinstructions stored on the computer readable storage media, to: identifya first set of database tables based on a set of access patterns storedin a computer storage device, the first set of database tables having afirst format, and the set of access patterns indicating a set of userdata queries, insertions, updates or deletions to data stored in thefirst set of database tables; provide a second set of database tableshaving a second database format corresponding to the first set ofdatabase tables; create a mapping between the first set of databasetables and the second set of database tables; and generate a key for thesecond set of database tables based on at least one condition of the setof access patterns.

A fourth aspect of the present invention provides a method for deployinga system for converting database formats based on user data accesspatterns in a networked computing environment, comprising: providing acomputer infrastructure being operable to: identify a first set ofdatabase tables based on a set of access patterns stored in a computerstorage device, the first set of database tables having a first format,and the set of access patterns indicating a set of user data queries,insertions, updates or deletions to data stored in the first set ofdatabase tables; provide a second set of database tables having a seconddatabase format corresponding to the first set of database tables;create a mapping between the first set of database tables and the secondset of database tables; and generate a key for the second set ofdatabase tables based on at least one condition of the set of accesspatterns.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features of this invention will be more readilyunderstood from the following detailed description of the variousaspects of the invention taken in conjunction with the accompanyingdrawings in which:

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 depicts a system diagram according to an embodiment of thepresent invention.

FIG. 5 depicts a process/component flow diagram according to anembodiment of the present invention.

FIG. 6 depicts a method flow diagram according to an embodiment of thepresent invention.

The drawings are not necessarily to scale. The drawings are merelyschematic representations, not intended to portray specific parametersof the invention. The drawings are intended to depict only typicalembodiments of the invention, and therefore should not be considered aslimiting the scope of the invention. In the drawings, like numberingrepresents like elements.

DETAILED DESCRIPTION

Illustrative embodiments will now be described more fully herein withreference to the accompanying drawings, in which embodiments are shown.This disclosure may, however, be embodied in many different forms andshould not be construed as limited to the embodiments set forth herein.Rather, these embodiments are provided so that this disclosure will bethorough and complete and will fully convey the scope of this disclosureto those skilled in the art. In the description, details of well-knownfeatures and techniques may be omitted to avoid unnecessarily obscuringthe presented embodiments.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of this disclosure.As used herein, the singular forms “a”, “an”, and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. Furthermore, the use of the terms “a”, “an”, etc., do notdenote a limitation of quantity, but rather denote the presence of atleast one of the referenced items. The term “set” is intended to mean aquantity of at least one. It will be further understood that the terms“comprises” and/or “comprising”, or “includes” and/or “including”, whenused in this specification, specify the presence of stated features,regions, integers, steps, operations, elements, and/or components, butdo not preclude the presence or addition of one or more other features,regions, integers, steps, operations, elements, components, and/orgroups thereof.

Embodiments of the present invention relate to approaches for conversionbetween database formats (e.g., from a relational database format to ahash table or a “big table” database format) based on user data accesspatterns (e.g., a set of queries) in a networked computing environment(e.g., a cloud computing environment). In a typical embodiment, a firstset of database tables having a first format is identified based on aset of access patterns stored in a computer storage device. A second setof database tables having a second database format corresponding to thefirst set of database tables may then be provided (e.g., accessed,augmented and/or generated). A mapping between the first set of databasetables and the second set of database tables may then be created. Acolumn set may then be generated based on at least one condition of theset of access patterns. The column set may then be used as a key for thesecond set of database tables.

In general, a hash table or “big table” database format maps twoarbitrary string values (e.g., row key and column key) and timestamp(hence a three dimensional mapping) into an associated arbitrary bytearray. The big table format may be defined as a sparse, distributedmulti-dimensional sorted map. A big table format is generally designedto scale across a high volume of machines and make it more convenient toadd more machines without significant reconfiguration. Each table mayhave multiple dimensions (e.g., one of which is a field for time toallow for versioning and “garbage” collection). Tables may be optimizedfor various file systems by being split into multiple tablets (e.g.,segments of the tables may be split along a row chosen such that thetablet will be a certain size (e.g., about 200 megabytes).

When sizes of tables have a potential to grow beyond a specified limit,the tablets may compressed using various algorithms (e.g., BMDiff, theZippy compression algorithm (open-sourced as Snappy). Locations intablets may be recorded as database entries in multiple special tablets,which are called “META1” tablets. META1 tablets may be found by queryinga “META0” tablet, which typically resides on a server of its own sinceit may often be queried by clients as to the location of the “META1”tablet. Along these lines, the META0 server is not generally abottleneck, since the processor time and bandwidth necessary to discoverand transmit META1 locations is typically minimal, and clients may cachelocations to minimize queries.

It is understood in advance that although this disclosure includes adetailed description of cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded, automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active consumer accounts). Resource usage canbe monitored, controlled, and reported providing transparency for boththe provider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based email). Theconsumer does not manage or control the underlying cloud infrastructureincluding network, servers, operating systems, storage, or evenindividual application capabilities, with the possible exception oflimited consumer-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication-hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10, there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM, or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

The embodiments of the invention may be implemented as a computerreadable signal medium, which may include a propagated data signal withcomputer readable program code embodied therein (e.g., in baseband or aspart of a carrier wave). Such a propagated signal may take any of avariety of forms including, but not limited to, electro-magnetic,optical, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that can communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium including, but not limited to, wireless,wireline, optical fiber cable, radio-frequency (RF), etc., or anysuitable combination of the foregoing.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a consumer to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via I/O interfaces22. Still yet, computer system/server 12 can communicate with one ormore networks such as a local area network (LAN), a general wide areanetwork (WAN), and/or a public network (e.g., the Internet) via networkadapter 20. As depicted, network adapter 20 communicates with the othercomponents of computer system/server 12 via bus 18. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with computer system/server 12.Examples include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as private, community,public, or hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms, and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include mainframes. In oneexample, IBM® zSeries® systems and RISC (Reduced Instruction SetComputer) architecture based servers. In one example, IBM pSeries®systems, IBM System x® servers, IBM BladeCenter® systems, storagedevices, networks, and networking components. Examples of softwarecomponents include network application server software. In one example,IBM WebSphere® application server software and database software. In oneexample, IBM DB2® database software. (IBM, zSeries, pSeries, System x,BladeCenter, WebSphere, and DB2 are trademarks of International BusinessMachines Corporation registered in many jurisdictions worldwide.)

Virtualization layer 62 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 64 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.Consumer portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provides pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA. Further shown in management layer is databaseformat conversion, which represents the functionality that is providedunder the embodiments of the present invention.

Workloads layer 66 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and consumer data storage and backup. As mentioned above,all of the foregoing examples described with respect to FIG. 3 areillustrative only, and the invention is not limited to these examples.

It is understood that all functions of the present invention asdescribed herein typically may be performed by the database formatconversion functionality (of management layer 64, which can be tangiblyembodied as modules of program code 42 of program/utility 40 (FIG. 1).However, this need not be the case. Rather, the functionality recitedherein could be carried out/implemented and/or enabled by any of thelayers 60-66 shown in FIG. 3.

It is reiterated that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather, theembodiments of the present invention are intended to be implemented withany type of networked computing environment now known or laterdeveloped.

As indicated above, embodiments of the present invention enable bigtable creation based on a relational database model (e.g., expressed ina ddl or xml schema file) and data access patterns. In general, theembodiments of the present invention will leverage users data accesspatterns (e.g., queries, insertions, updates or deletions) to create bigtables. That is, data (from table and joints) involved in queries,insertions, updates or deletions is examined/analyzed and used toconstruct big tables.

Referring now to FIG. 4, a system diagram describing the functionalitydiscussed herein according to an embodiment of the present invention isshown. It is understood that the teachings recited herein may bepracticed within any type of networked computing environment 86 (e.g., acloud computing environment 50). A computer system/server 12, which canbe implemented as either a stand-alone computer system or as a networkedcomputer system is shown in FIG. 4. In the event the teachings recitedherein are practiced in a networked computing environment 86, eachclient need not have a database format conversion engine (engine 70).Rather, engine 70 could be loaded on a server or server-capable devicethat communicates (e.g., wirelessly) with the clients to providedatabase format conversion therefor. Regardless, as depicted, engine 70is shown within computer system/server 12. In general, engine 70 can beimplemented as program/utility 40 on computer system 12 of FIG. 1 andcan enable the functions recited herein. As further shown, engine 70 (inone embodiment) comprises a rules and/or computational engine thatprocesses a set (at least one) of rules/logic 72 and/or providesdatabase format conversion hereunder.

Along these lines, engine 70 may perform multiple functions similar to ageneral-purpose computer. Specifically, among other functions, engine 70may (among other things): identify a first set of database tables 80A-N(e.g., stored in one or more computer storage devices 78) having a firstformat (e.g., a relational database) based on a set of access patterns74A-N (e.g., although not shown, set of access patterns 74A-N may alsostored and/or accessed from one or more computer storage devices), theset of access patterns 74A-N a set of user data queries, insertions,updates, and/or deletions to data stored in first set of database tables80A-N; provide a second set of database tables 82A-N having a seconddatabase format (e.g., a big/hash table database format) correspondingto the first set of database tables 80A-N; generate a set ofde-normalized tables from the first set of database tables 80A-N;augment at least one table of the first set of database tables 80A-N toyield the second set of database tables 82A-N; create a mapping 76between the first set of database tables 80A-N and the second set ofdatabase tables 82A-N; generate a key for the second set of databasetables based on at least one condition of the set of access patterns;generate a column set based on at least one condition of the set ofaccess patterns 74A-N; use the column set as a key for the second set ofdatabase tables 82A-N; and/or propagate at least one data operation(e.g., a data deletion operation, a data insertion operation, etc.) tothe second set of database tables 82A-N based on at least one of: thekey, the mapping, or the at least one condition.

Referring now to FIG. 5, a component flow diagram according to oneembodiment of the present invention is shown. It is understood inadvance that one or more of the components shown in FIG. 5 may beimplemented via or in conjunction with any of the components of FIGS.1-4 (e.g., program 40 of FIG. 1, engine 70 and/or computer storagedevice 78 of FIG. 4, etc.) Regardless, as shown, FIG. 5 generally showsthe following components:

Relational data model and access pattern 100: A relational data modelcan be represented by data definition language (DDL). Access patternsgenerally represents one or more operations against the relational datamodel (e.g., queries, data insert operations, data update operations,data deletion operations, etc.)

Big table solution generator 102: By taking the relational data modeland the access pattern as input, two or more objects may generated suchas: a cloud data table repository configuration and hash tabledefinition 104 that defines big table name, table structure (e.g. hashtable definition, key definition, etc.); and/or a data serviceapplication programming interface (API) definition and implementationlogic 106 (e.g., a method and /or logic that implements the accesspattern).

Cloud data server 110: By deploying data service API definition andimplementation logic 106, cloud data server 110 may provide a dataservice for one or more client applications 108. Such service mayinclude operations that are described in the access pattern.

Cloud data repository 112: A computer storage device that contains datathat client applications utilize.

In general, the process through components of FIG. 5 may proceed asfollows:

Step P1: Based on relational data model and access patterns 100, bigtable solution generator 102 may create cloud data repositoryconfiguration and hash table definition 104 and data service APIdefinition and implementation logic 106.

Step P2: Cloud data repository configuration and hash table definition104 is deployed into cloud data repository 112.

Step P3: Data service API definition and implementation logic 106 isdeployed into cloud data server 110.

Step P4: Client application 108 issues a data service request throughAPIs deployed in cloud data server 110.

Step P5: Cloud data server 110 facilitates access to data stored incloud data repository 112.

Step P6: Cloud data repository 112 communicates requested data to clouddata server 110.

Step P7: Cloud data server 110 returns a service request result toclient application 108.

ILLUSTRATIVE EXAMPLE

This section will describe an illustrative algorithm/process forcarrying out at least one embodiment of the present invention. Thealgorithm will utilize the following input and/or output:

INPUT: ER model and access pattern (e.g. queries, insertions, updates,or deletions, etc.)

OUTPUT: big table model

Step 1:

-   FOR each query,    -   (1) Identify the corresponding relational tables based on the        tables specified in the “FROM” clause,        -   (1.1) IF an existing big table covers the identified            relational tables, go to step (2).        -   (1.2) IF an existing big table partially covers the            identified relational tables, e.g., the big/hash table is            formed by Table A joined with Table B, the identified            relational tables include Table A, Table B and Table C, then            the existing big table should be augmented by the identified            tables that have not been covered, such as Table C.        -   (1.3) OTHERWISE, generate a de-normalized table out of these            identified relational tables, which form a new big table.

In addition, the mapping between each of the identified relationaltables and the updated or newly generated big table is created.

-   -   (2) Create a column family for the columns mentioned in the        specified query conditions, including “=”, “>”, “<” conditions.        In addition, corresponding secondary indices are needed to        ensure search performance. For example, in a condition like        “StudentID=‘001’”, an secondary index should be built on the        column StudentID    -   (3) Utilize the column family as the key for the big table.

It may be noted that at the end of this step, if a relational databasetable has not been mapped to a big table, then a big table with thattable will be created and the corresponding mapping will be created.

-   Step 2:

FOR each delete operation:

-   -   (1) Identify the mapped big tables for the involved relational        table in the delete operation.    -   (2) FOR each big table, check the condition of the delete        operation        -   (2.1) IF the key to access the big table can support the            condition running, go to the next big table;        -   (2.2) ELSE create a key to access the big table as described            in Step 1 above.            It may be noted that once the mapping is created, one or            more data operations may be inserted as follows:

-   FOR each insert operation:    -   (1) Identify the mapped big tables for the involved relational        table in the insert operation.    -   (2) FOR each identified big table        -   (2.1) FOR each record in the big table, check the            compatibility between the to-be-inserted record and the big            table record. For example, suppose this big table is formed            by the join between Table A and Table B, the join condition            is A.StudentID=B.StudentID. Suppose now we need to insert a            record into Table A. Then we need to check if the StudentID            column in newly inserted record has the same value as the            StudentID field in the big table record. IF they are            compatible, then we can insert a new record into the big            table with the to-be-inserted record joined with the rest of            the fields in the big table record.        -   (2.2) IF there is no record in the big table which is            compatible with the to-be-inserted record, we will add a new            record into the big table with only the fields from the            to-be-inserted record.            Update can be done by a delete and an insert, so its            operation is similar o insertion and deletion.

Referring now to FIG. 6, a method flow diagram according to anembodiment of the present invention is shown. In step S1, a first set ofdatabase tables having a first format (e.g., a relational databaseformat) based on a set of user access patterns stored in a computerstorage device is identified (e.g., the set of user access patternsindicating a set of user data retrieval operations to data stored in thefirst set of database tables). In step S2, a second set of databasetables having a second database format (e.g., big table format)corresponding to the first set of database tables is provided. In stepS3, a mapping between the first set of database tables and the secondset of database tables is created. In step S4, a column set is generatedbased on at least one condition of the set of user access patterns. Instep S5, the column set is used as a key for the second set of databasetables.

While shown and described herein as a database format conversionsolution, it is understood that the invention further provides variousalternative embodiments. For example, in one embodiment, the inventionprovides a computer-readable/useable medium that includes computerprogram code to enable a computer infrastructure to provide databaseformat conversion functionality as discussed herein. To this extent, thecomputer-readable/useable medium includes program code that implementseach of the various processes of the invention. It is understood thatthe terms computer-readable medium or computer-useable medium compriseone or more of any type of physical embodiment of the program code. Inparticular, the computer-readable/useable medium can comprise programcode embodied on one or more portable storage articles of manufacture(e.g., a compact disc, a magnetic disk, a tape, etc.), on one or moredata storage portions of a computing device, such as memory 28 (FIG. 1)and/or storage system 34 (FIG. 1) (e.g., a fixed disk, a read-onlymemory, a random access memory, a cache memory, etc.).

In another embodiment, the invention provides a method that performs theprocess of the invention on a subscription, advertising, and/or feebasis. That is, a service provider, such as a Solution Integrator, couldoffer to provide database format conversion functionality. In this case,the service provider can create, maintain, support, etc., a computerinfrastructure, such as computer system 12 (FIG. 1) that performs theprocesses of the invention for one or more consumers. In return, theservice provider can receive payment from the consumer(s) under asubscription and/or fee agreement and/or the service provider canreceive payment from the sale of advertising content to one or morethird parties.

In still another embodiment, the invention provides acomputer-implemented method for database format conversion. In thiscase, a computer infrastructure, such as computer system 12 (FIG. 1),can be provided and one or more systems for performing the processes ofthe invention can be obtained (e.g., created, purchased, used, modified,etc.) and deployed to the computer infrastructure. To this extent, thedeployment of a system can comprise one or more of: (1) installingprogram code on a computing device, such as computer system 12 (FIG. 1),from a computer-readable medium; (2) adding one or more computingdevices to the computer infrastructure; and (3) incorporating and/ormodifying one or more existing systems of the computer infrastructure toenable the computer infrastructure to perform the processes of theinvention.

As used herein, it is understood that the terms “program code” and“computer program code” are synonymous and mean any expression, in anylanguage, code, or notation, of a set of instructions intended to causea computing device having an information processing capability toperform a particular function either directly or after either or both ofthe following: (a) conversion to another language, code, or notation;and/or (b) reproduction in a different material form. To this extent,program code can be embodied as one or more of: an application/softwareprogram, component software/a library of functions, an operating system,a basic device system/driver for a particular computing device, and thelike.

A data processing system suitable for storing and/or executing programcode can be provided hereunder and can include at least one processorcommunicatively coupled, directly or indirectly, to memory elementsthrough a system bus. The memory elements can include, but are notlimited to, local memory employed during actual execution of the programcode, bulk storage, and cache memories that provide temporary storage ofat least some program code in order to reduce the number of timescodemust be retrieved from bulk storage during execution. Input/outputand/or other external devices (including, but not limited to, keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening device controllers.

Network adapters also may be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems,remote printers, storage devices, and/or the like, through anycombination of intervening private or public networks. Illustrativenetwork adapters include, but are not limited to, modems, cable modems,and Ethernet cards.

The foregoing description of various aspects of the invention has beenpresented for purposes of illustration and description. It is notintended to be exhaustive or to limit the invention to the precise formdisclosed and, obviously, many modifications and variations arepossible. Such modifications and variations that may be apparent to aperson skilled in the art are intended to be included within the scopeof the invention as defined by the accompanying claims.

What is claimed is:
 1. A computer-implemented method for convertingdatabase formats based on user data access patterns in a cloud computingenvironment, comprising: identifying a first set of database tablesbased on a set of access patterns stored in a computer storage device,the first set of database tables having a first format, wherein thefirst format is a relational database format; augmenting at least onetable of the first set of database tables to generate a second set ofdatabase tables having a second format and stored in a cloud datarepository, the second set of database tables being a set ofde-normalized tables; creating a mapping between the first set ofdatabase tables and the second set of database tables; generating a keyfor the second set of database tables based on at least one condition ofthe set of access patterns; and propagating at least one data operationto the second set of database tables based on the key.
 2. Thecomputer-implemented method of claim 1, the second format being a hashtable format.
 3. The computer-implemented method of claim 1, thegenerating a key comprising: generating a column set based on the atleast one condition of the set of access patterns; and using the columnset as the key for the second set of database tables,
 4. Thecomputer-implemented method of claim 1, wherein the propagating isfurther based on the mapping.
 5. The computer-implemented method ofclaim 1, wherein the propagating is further based on the at least onecondition.
 6. The computer-implemented method of claim , the at leastone data operation being at least one of: a data query operation, a dataupdate operation, a data deletion operation, or a data insertionoperation.
 7. The computer-implemented method of claim 1, wherein asolution service provider provides a computer infrastructure operable toperform the method.
 8. A system for converting database formats based onuser data access patterns in a cloud computing environment, comprising:a memory medium comprising instructions; a bus coupled to the memorymedium; and a processor coupled to the bus that when executing theinstructions causes the system to: identify a first set of databasetables based on a set of access patterns stored in a computer storagedevice, the first set of database tables having a first format, whereinthe first format is a relational database format; augment at least onetable of the first set of database tables to generate a second set ofdatabase tables having a second format and stored in a cloud datarepository, the second set of database tables being a set ofde-normalized tables; create a mapping between the first set of databasetables and the second set of database tables; generate a key for thesecond set of database tables based on at least one condition of the setof access patterns; and propagate at least one data operation to thesecond set of database tables based on the key.
 9. The system of claim8, the second format being a hash table format.
 10. The system of claim8, the memory medium further comprising instructions for causing thesystem to: generate a column set based on the at least one condition ofthe set of access patterns. use the column set as the key for the secondset of database tables.
 11. The system of claim 10, the memory mediumfurther comprising instructions for using the column set as the key forthe second set of database tables.
 12. The system of claim 8, thepropagating being further based on the mapping.
 13. The system of claim8, the propagating being further based on the at least one condition.14. The system of claim 8, the at least one data operation being atleast one of: a data query operation, a data update operation, a datadeletion operation, or a data insertion operation.
 15. A computerprogram product for converting database formats based on user dataaccess patterns in a cloud computing environment, the computer programproduct comprising a computer readable storage media, and programinstructions stored on the computer readable storage media, to: identifya first set of database tables based on a set of access patterns storedin a computer storage device, the first set of database tables having afirst format, wherein the first format is a relational database format;augment at least one table of the first set of database tables togenerate a second set of database tables having a second format andstored in a cloud data repository, the second set of database tablesbeing a set of de-normalized tables; create a mapping between the firstset of database tables and the second set of database tables; generate akey for the second set of database tables based on at least onecondition of the set of access patterns; and propagate at least one dataoperation to the second set of database tables based on the key.
 16. Thecomputer program product of claim 15, the second format being a bigtable format.
 17. The computer program product of claim 15, the computerreadable storage media further comprising instructions to generate acolumn set based on the at least one condition of the set of accesspatterns.
 18. The computer program product of claim 17, the computerreadable storage media further comprising instructions to use the columnset as the key for the second set of database tables.
 19. The computerprogram product of claim 15, the propagating being further based on atleast one of: the mapping, or the at least one condition.
 20. Thecomputer program product of claim 15, the at least one data operationbeing at least one of: a data query operation, a data update operation,a data deletion operation, or a data insertion operation